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Remote homology detection among proteins in an unsupervised approach from sequences is an important problem in computational biology. The existing neighborhood cluster kernel methods and Markov clustering algorithms are most efficient for homolog detection. Yet they deviate from random walks with inflation or similarity depending on hard thresholds. Our spectral clustering approach with new combined...
Data clustering is one of the powerful techniques for the knowledge discovery from data. In this paper, a novel approach for hierarchical clustering has been proposed over non-binary search space. Besides the agglomerative methods, the proposed algorithm has considered the Strength of Presence associated with each transaction, to yield quality clusters which are again more close to the real life situation...
Point symmetry-based clustering is an important unsupervised learning tool for recognizing symmetrical convex or non-convex shaped clusters, even in the microarray datasets. To enable fast clustering of this large data, in this article, a distributed space and time-efficient scalable parallel approach for point symmetry-based K-means algorithm has been proposed. A natural basis for analyzing gene...
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